Graphic Processing Units (GPUs) are becoming increasingly important to many server applications, particularly for performing Virtual Desktop Infrastructure (VDI) and big data analysis implementations. However, providing and consuming GPUs in a computing environment creates a unique set of issues, particularly in a highly agile, virtualized environment. GPUs are typically not available for blade server type arrangements of computing environments, generally requiring organizations to implement non-standard rack-mount servers alongside their blade servers to provide the GPU services for the computing environment. Further, GPUs are expensive investments, so an organization will often deploy GPUs to a selective subset of their servers such that workloads requiring GPU support have to be specifically scheduled on those rack-mount servers that are provisioned with GPUs. However, this reduces flexibility of workload deployment, and once a virtualized workload has started running on a particular server, it is pinned to the server, and cannot be migrated to an alternate server for performance or power management reasons. Furthermore, even though a typical server may be capable of hosting a hundred or more VDI sessions, a GPU dependency for a VDI workload will often reduce this capability to around 30 sessions, resulting in a substantial reduction in the efficiencies of the VDI capability.